The three open Phase 5 findings were one defect: the stand-in was keyed on
CI Server POINT names (PS_STN_WET_WELL_LEVEL) when the historian is keyed on
CI Server ITEM names (AID.WRPS.STN.LEVEL). Modbus carries register numbers,
not names, so those two layers are free to differ - and do. Reconciling
against the register map, as planned, would only have proved the first three
namespaces agreed with each other.
Rebuilt from WRPS/05-scada/modbus, so item names, sample rates, retention and
timestamp semantics come from the machine rather than from a guess.
(a) Level tag does not join. PS_STN_WET_WELL_LEVEL becomes a tag row in its
own right; LIT-101 is marked NOT HISTORISED - a field input on %IW0 that
never reaches SCADA. It was the only seed row carrying two addresses.
public.historian_items holds the item-to-tag mapping, generated by
scripts/gen_historian_items.py and enforced non-empty at generate, at
deploy and at verify.
(b) first_alarm/last_alarm returned UTC. Converted inside the measure, so it
stays in Cube and happens once. Aggregate first, convert after - the other
order picks the wrong row across a DST fall-back. Returned as a formatted
string with a companion site_timezone measure. Storage being UTC is now
confirmed, not assumed: all 49 points carry TIME_ZONE "Date+time GMT" and
every history group CORRECT_DAYLIGHT=0. This answers Phase 4 task 4.
(c) High level alarm filed against the wrong equipment. Both sides were right
about different things; the defect was asserting equipment twice. The
history now carries no equipment column at all - faithful, since CI
Server's section tree stops at the station and three pumps. Equipment is
reached bit -> tag -> equipment via public.alarm_bits.
Alarms are derived, not stored: CI Server's ALARM_HISTORY group is empty
because every item imports with alarming off. Decomposing the alarm word needs
no configuration that does not exist.
Three things the SCADA config changed that were never filed as faults:
- retention is 7 days, not 30. The advisory path was reporting a month of
evidence drawn from a week of data
- the analogue rate is 5 s, not 60. Two measures multiplied sample counts by
a hardcoded 60 - a twelvefold overstatement that read as plausible
- the deadband warning in process_values.yml was wrong and was steering
people away from the correct measure
db/002_fixtures.sql now asserts its own counts at load and cross-checks the
alarm derivation against two independent signals. Those prove the pipeline,
not the plant.
db/README-standin-historian.md documents removal: the seam between generation
and contract, and twelve assumptions about imh that are NOT confirmed. Two of
them fail silently.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Two retrieval faults, both only visible once a real model ran.
find_procedure ranked a procedure's chunks by similarity to the question. For
"how do I lift the interlock on Pump 02" the closest chunks ARE the step list -
so the branch whose entire purpose is not reproducing steps was handing the
answer writer nothing but steps, while omitting the header block carrying the
title and the authorising role. The model was being asked for a title it had
never been shown, and returned "".
Once the document is identified, WHICH document it is settles what to send:
the header and the prerequisites, in document order, never the steps.
STEP_SECTION_RE is a second line behind ProceduralAnswer's instruction-language
check, not a replacement for it - the contract still rejects instruction
language whatever arrives here. This removes the temptation rather than relying
on catching it.
Separately, rerank did 0.75 * chunk.similarity where similarity is NULL for a
chunk ingested with --no-embed: `1 - (NULL <=> vec)` is NULL, so it raised
TypeError and 500'd the whole question rather than ranking that chunk last. It
now degrades to the lexical half - an unembedded chunk is still findable, just
not by meaning - and Chunk.similarity is typed honestly as float | None.
find_procedure_lexical takes the same identify-then-expand shape, so the stub
keeps testing the shape it always did.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Azure OpenAI is pending and imh is pending, so POST /ask could not return
anything at all - which left the entire chain either side of the model
unproven: the browser, the API, entity resolution, Cube, retrieval, the
contracts, the banners, the error paths. All of it is testable now, and waiting
for a key to find out whether it works is a choice to find out later.
NO_LLM_STUB=true substitutes the two steps that need a model and nothing else.
- Classification: the caller supplies the class, from a dropdown in the UI.
NOT a keyword classifier. A crude keyword classifier produces a PLAUSIBLE
label, and a plausible wrong label is the exact failure this system exists
to prevent - "how do I reset it" landing in Historical is how a synthesised
procedure reaches an operator. Choosing by hand is honest about what is
happening and drives each branch deliberately. apply_safety_rules() still
runs over the result.
- Prose: a fixed placeholder per class, in stub.py.
Everything else is the real path. This is possible because generate() already
kept the factual fields away from the model: rows, counts, citations, the
fixture flag and the class are attached from evidence, and only prose comes
from the generator. Splitting that into _generate_prose() and _assemble() makes
the seam explicit - the stub feeds _assemble() exactly as the model does, so
this is a fair test of the assembly path rather than a mock of it.
The contracts are the point. A stub payload goes through enforce_contract()
unchanged, and it FAILED first time on two classes: the "nothing found" wording
did not match the not-found detectors, so Reference and Procedural returned 422
rather than an uncited answer. That is the contract doing its job against text
no model wrote. Retries are pointless on deterministic output, and a 422 is a
real result here, not a stub bug.
Retrieval is lexical (retrieval.lexical_search), because embedding the question
needs the model. Kept beside search() and never called on the normal path, so
nobody reads a trace and mistakes a lexical hit for a semantic one. It matches
what the operator typed, not what they meant.
What it does not prove: whether the classifier would have labelled correctly -
a person did; whether retrieval finds the RIGHT chunk; and nothing about prose.
It also cannot fill prerequisites_verbatim - extracting them with a regex would
be the "synthesised from fragments" failure the Procedural contract forbids, so
the list is empty and the answer says so.
Every answer carries stub_mode: true in the contract, not decorated on by the
UI, and a banner beside the fixture banner. Same reasoning: an answer nobody
generated must not be indistinguishable from one that was.
Also here:
- demo/ai-docs: three fabricated documents, numbered WRPS-DEMO-00x so header
extraction is genuinely exercised against a number no real WRPS document
can have. Their setpoints contradict tags.csv on purpose.
- VITE_API_BASE build arg, for a tunnelled build before DNS exists. The
tunnel origin is allowed in CORS only while NO_LLM_STUB is on, so it
disappears with the flag. Proxying /api through ai-web's nginx would have
been easier and was rejected: it creates a second route to the API that
bypasses the api.yokogawa.tech Caddy block, where the Phase 9 publisher
rule lives.
Verified on lin001 with no Azure key set at all: all five classes return 200
through the real UI in a browser, over an SSH tunnel, with citations from the
demo documents, real Cube numbers, and both banners showing.
Turning it off: NO_LLM_STUB=false in ~/ai/api.env, restart ai-api.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Every Historical and Advisory answer stated a window in AEST and queried one
shifted by ten hours.
rolling_window() builds its boundary strings in SITE_TIMEZONE - that is the
whole point of it, and its docstring says so. metrics.run() then posted the
query to Cube with no timezone at all, and Cube defaults to UTC. So
"2026-08-14T15:22:13" meant 15:22 Sydney to the code that produced it and 15:22
UTC to the engine that ran it, and MetricResult.time_window reported
SITE_TIMEZONE from config rather than whatever the query actually used, so the
two could not disagree visibly.
Measured on the fixtures, same dateRange, one field changed:
timezone UTC 8019 samples
timezone Australia/Sydney 8619 samples
600 samples. One per minute, ten hours, exactly the offset.
Nothing about the answer looked wrong. The prose was right, the count was a
real count, the window description was correctly formatted and correctly named
AEST. It was only visible by reading the Cube query in the UI's "show working"
panel - which is an argument for that panel existing, and an argument for
looking at the thing in a browser rather than trusting curl against the API.
- check_cube_query() now takes site_timezone and pins it onto the query, at
the single point every Cube query passes through. Per-query-builder is the
wrong place: "remember to set the timezone" is not a control, and this
defect is what forgetting looks like. An explicit timezone already on the
query is left alone.
- time_window now reports capped["timezone"] - the timezone the query ran in,
not the one it should have run in.
An unpinned timezone belongs in the same guardrail as an unpinned date range,
and for the same reason: both make an answer unreproducible. The difference is
that an unpinned date range is obvious in the query and an unpinned timezone
is invisible.
eval case H28 records it. Two unit tests: the timezone is pinned, and an
explicit one is not overridden.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Build spec and host brief carried in from C:\Claude and WRPS/02-env; the
plant model (equipment, tags, alarm bitmask, enums, unit conversions) is
derived from WRPS/04-plc/register-map.csv, WRPS/05-scada/modbus/scada-points.csv
and WRPS-CTL-003.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude